Economic Impact of BACS and TBM Systems on Residential Buildings

Transcription

1 Economic Impact of BACS and TBM Systems on Residential Buildings E. Riva Sanseverino*, G. Zizzo*, and D. La Cascia* * DEIM Università di Palermo, viale delle Scienze, Palermo, (Italy) Abstract The paper presents the results of a study on the economic impact of building automation control systems and technical building management systems on residential buildings. The different functions considered by European Standard EN and having impact on the energy performance of buildings are applied to a test house, varying its energy efficiency class. The economic impact due to the introduction of each BACS or TBM function is evaluated using the BAC factors method, considering the real costs for the purchase and the installation of the components that implement the specific functions and the yearly energy costs of the building before and after their installation. Index Terms BAC factors, TBM systems, EN I. INTRODUCTION The European Performance in Building Directive (EPBD) 2010/31/EU [1] promotes the improvement of the energy performance of buildings within the EU Member States. The term energy performance indicates the amount of energy actually consumed (or estimated for new buildings) to meet the different needs associated with building heating and cooling, ventilation, lighting, etc. All the EU Member States have defined methodologies for the calculation of the energy performance of buildings on the basis of the general framework set out by the EPBD. The energy performance depends on constructive, thermal but also electrical factors. These last are: - the presence of electrical energy generation systems based on renewable energy sources (RES); - the electrical energy produced by Combined Heat and Power (CHP) systems; - the presence of building automation control systems an technical building management systems. It is important to underline how the current edition of the EPBD gives greater importance to automation, control and monitoring systems as compared to the old EPBD [3]. In particular the new edition of the EPBD encourages the introduction of intelligent metering systems and the use of active control systems such as automation, control and monitoring systems for energy saving purposes whenever a building is constructed or undergoes major renovation in line with Directive 2009/72/EC [2]. This evolution is perfectly in line with the attention given by European technical committees to the theme of evaluating the contribution that automation gives to the energy performance of buildings. Indeed in 2007 the European Standard EN [4] was issued to devise terminology, rules and methods for the estimation of the impact of building automation and control systems (BACS) and technical building management (TBM) on energy performance and energy use in both existing and new or renovated buildings. The standard defines a list of BACS and TBM functions which have an impact on the energy performance of buildings; it also defines a detailed and an approximated method to assess the impact of these functions. Moreover, EN introduces four different BAC efficiency classes based on the implemented functions: - A, corresponding to high energy performance BACS and TBM systems; - B, corresponding to advanced BACS and TBM systems; - C, corresponding to standard BACS; - D, corresponding to non-energy efficient BACS. These classes do not refer to the building as a whole, but only to the installed BACS and TBM systems, therefore they are not correlated to the energy classes of a building defined by the European Standard EN [5]. As a consequence, the installation costs of systems implementing BACS or TBM functions are not correlated with the operational costs of the building, strongly dependent on its energy class. In this paper, it is presented a study that, for a medium category single-family house, correlates the cost for improving the BAC efficiency class according to the standard EN from D to A with the economic return due to the reduction of the energy costs, using the BAC factor method proposed in [4]. Purpose of the paper is to show how the promotion of BACS and TBM systems, although entails high initial costs for households, it is as more suitable as lower it is the starting energy class of the house. The installation costs of the BACS and TBM systems have been evaluated with reference to the Italian market and are composed of the purchase cost of the components, the installation cost and the cost of wiring. The energy class of the house has been evaluated according to the Italian regulations [6]-[8] and technical standard EN ISO [9], changing the thermal /13/$ IEEE 591

2 resistance of floor, walls, roof and windows and obtaining, in this way, different classes according to [5] from G to A with different energy consumption. Finally, the electric energy consumption of the house is calculated according to the methodology proposed in [10]. II. THE BAC FACTORS METHOD The European Standard EN proposes two different methods for calculating the impact of the building automation and management functions on the energy performance: - the detailed method, that requires a deep knowledge of the characteristic of the building and of the installed lighting, cooling and thermal systems; - the BAC Factors method, that allows a rough estimation of the impact of the BACS and TBM systems on the energy performance of the building in a reference period of one year. According to this last method the influence of automation functions on buildings is quantified through energy efficiency factors, named BAC factors, calculated by comparing the yearly energy consumption of a system (cooling, lighting, etc.) of a reference class C building with the consumption of the same system calculated in the same working conditions (occupation time, load profile, weather, solar irradiation, etc.) after the application of a BAC/HBES automation system in the different classes (A, B, C, D). In Table I are reported the BAC efficiency factors for thermal energy (heating and cooling systems) f BAC,hc for residential building depending on the efficiency class the BAC/TBM system is related to. The factor for class C are assumed equal to 1. TABLE I BAC EFFICIENCY FACTORS FOR THERMAL ENERGY FOR RESIDENTIAL BUILDINGS Residential Building BAC efficiency factor BAC,hc A B C D Single family houses Apartment block Other residential buildings In Table II are reported the BAC efficiency factors for electric energy f BAC,e for residential building depending on the efficiency class the BAC/TBM system is related to. Also in this case the factor for class C is assumed equal to 1. TABLE II BAC EFFICIENCY FACTORS FOR ELECTRIC ENERGY FOR RESIDENTIAL BUILDINGS Residential Building BAC efficiency factor f BAC,e A B C D Single family houses Apartment block Other residential buildings In Table III is represented how to apply the BAC factors method for calculating the reduction of the energy requested by a building upgrading the BAC efficiency class from a starting to a better class c.2. Demand Losses BAC factor from class c.1 to class c.2 class c.2 reduction Auxiliary Electric BAC factor from class c.1 to class c.2 Electric class c.2 Electric reduction TABLE III BAC FACTORS METHOD APPLICATION Heating Cooling Ventilation Lighting H1 C1 0 0 TL1 TL2 0 0 T = H1+C2+TL1+TL2+0+0 f BAC,hc f BAC,hc T (1-f BAC,hc) T AUX1 AUX2 AUX3 AUX4 E = AUX1+AUX2+AUX3+AUX4 f BAC,e f BAC,e E (1-f BAC,e) E III. METHODOLOGY FOR THE CALCULATION OF THE ELECTRIC ENERGY CONSUMPTION The yearly energy consumption is calculated starting from the daily power profile of the building. This is the sum of all the power profiles of the electric loads and is influenced by several factors: - the different working cycles of some devices (electric oven, dishwasher, washing machine, etc.); - the number of inhabitants of the house; - the period of the year (winter, summer or intermediate season). All these factors influence the number of uses per day and per week of a given electric load and are widely variable from house to house. For this reason it is not possible to determinate a unique daily power profile for the typical house. In recent years some methods have been defined [10]- [14] for simulating the daily power profile of residential 592

3 loads. The proposed methods are very similar and they all have in common the probabilistic approach to the problem and the construction of the daily power profile starting from the knowledge of the most relevant socioeconomic and demographic factors. Several probability functions cover the close relationship existing between the demand of residential customers and the psychological and behavioural factors typical of the household; the models make use of the latter through a Monte Carlo extraction process. In the present study, the daily power profile of the typical house is found according to [10]. IV. TEST HOUSE The test house is reported in fig. 1. It has about 100 m 2 useful floor area, indoor and outdoor lighting system and a conditioning system based on fan-coil units receiving hot or cold water by a thermal/cooling central. - Polisher (1000W); - Electric storage water heater (1800W). - Exhaust fan (150W); - Electric Iron (1200W); - Toaster (1000W); - Electric oven (2000W); - Microwave oven (2000W); - Dryer (2700W); - Fridge-freezer (350W); - Dishwasher (2000W); - Washing machine (2000W). Each electric load is characterized by a load profile. The load profile is the representation of the absorbed active power and of the related power factor, versus time. In the present study, according to the common practice for residential loads, loads power factors have been considered constant in time and equal to 0.9 (except for incandescent lamps for which a unitary power factor has been assumed). In the house are present: - a natural-gas boiler for winter heating (24000W); - a natural-gas chiller for summer cooling (15000W); - six fan-coils units. The starting BAC efficiency class is D. V. RESULTS OF THE STUDY In Table IV are reported the electric energy consumption for lighting and ventilation of the test house calculated according to [10]. For simplifying the calculations, the year has been divided into a summer period (from the 1 st of April to the 30 th of September) and a winter period (from the 1 st of October to the 31 st of March). Fig. 1. Floor plan of the test single-family house with indication of electrical power supplied devices. The characteristics of the supply are: - Single-phase system; - Rated Voltage and frequency: 230V/50Hz. The electric loads present in the house are very different and devoted to different applications: - Entrance lighting (1x60W incandescent lamp); - Living room lighting (3x18W fluorescent lamps); - Kitchen lighting (2x18W fluorescent lamps); - Corridor lighting (3x40W incandescent lamps); - Bathroom lighting (2 bathrooms each one having a total installed power equal to 100W); - Bedroom lighting (2 bedrooms each one having a total installed power equal to 100W); - Outdoor lighting (4x9W fluorescent lamps). - PC (120W); - HI-FI (300W); - TV, VCR and DVD player (400W); - Hairdryer (1200W); TABLE IV ELECTRIC ENERGY CONSUMPTION OF THE TEST HOUSE FOR LIGHTING AND VENTILATION Electric Winter period Electric Summer period Electric In Table V are reported the consumption for heating and cooling of the test house evaluated according to [9] varying the energy class from A to G. TABLE V THERMAL ENERGY CONSUMPTION OF THE TEST HOUSE VARYING THE ENERGY CLASS for Heating for Cooling A B C D E F G In Tables VI and VII are reported the energy savings 593

4 obtained improving the BAC efficiency class from D to A and calculated using the BAC factors method. TABLE VI ELECTRIC ENERGY SAVINGS IN THE PASSAGE OF THE BAC EFFICIENCY CLASS FROM D TO A Electric D Electric A Savings TABLE VII THERMAL ENERGY SAVINGS IN THE PASSAGE OF THE BAC EFFICIENCY CLASS FROM D TO A D A Savings A B C D E F G In Table VIII are reported the economic savings obtainable for the test house, varying the energy class from A to G. For the calculations, the electricity cost has been assumed equal to 0.19 /kwh [15] and the gas-natural cost has been assumed equal to /kwh [15]. TABLE VIII ECONOMIC SAVINGS OBTAINABLE FOR THE TEST HOUSE IN THE PASSAGE OF THE BAC EFFICIENCY CLASS FROM D TO A Economic Savings [ /year] A 139 B 169 C 199 D 228 E 257 F 287 G 315 In Table IX the components to install for improving the BAC efficiency class of the test house from D to A are summarized. In Table X the costs for the purchase and the installation of the components listed in Table IX are reported. The costs have been obtained as a results of a market analysis involving the most important producers of monitoring, control and automation systems in Europe. TABLE IX COMPONENTS TO INSTALL IN THE TEST HOUSE FOR TURNING THE BAC EFFICIENCY CLASS FROM D TO A TEMPERATURE Temperature probes in every room Temperature central unit Magnetic contacts and related interfaces for the detection of the open or closed position of doors and windows External temperature probe Actuators for fan-coil units LIGHTING Movement and lighting sensors in every room Dimmers SHUTTER Controls, displays, actuators for combined control of lighting, shutters, curtains and HVAC CENTRAL Central unit Power supply PC management software TABLE X COSTS FOR THE PURCHASE AND THE INSTALLATION OF THE COMPONENTS OF THE BAC OF THE TEST HOUSE Component Quantity Unit price [ ] price [ ] Power supply Contact interface Temperature probes Magnetic contacts Movement and lighting sensor Dimmer Actuators for shutters Actuators for fan-coils Control/actuators for lighting Display for scenario Central unit Bus Installation, general costs and gain of the contractor TOTAL The economic analysis is performed by calculating, on a period of 30 years, the cash flows (yearly costs and profits) and the Pay Back Period (PBP) of the investment for upgrading the BAC efficiency class. In order to carry out a realistic analysis, the cash flows are annualized considering a Weighted Average Cost of Capital (WACC) equal to 2.0%. Moreover the electricity cost and the gas-natural cost are assumed to increase of 4.0%/year. In Table XI the annualized cash flows are reported varying the efficiency class of the test house. In the examined case the PBP is in the range years and therefore very high. Nevertheless it is very important to underline that these values must not be considered universally valid. Indeed, PBP and cash flows depend strongly on the number, on the kind and on the market cost of the components of the BAC system installed, on the real 594

5 yearly energy savings depending by the kind of thermal/cooling system installed and by the daily management of the building. For example, lower PBP are obtainable if a diesel boiler is assumed to be installed in the test house instead of the natural-gas boiler. Therefore, the analysis carried out gives only general indications on the convenience of the installation of BAC systems. A general conclusion can be gather from the results of the calculations: the installation of a BAC system is as more convenient as higher is the electric and thermal consumption of the building and as lower is its energy class. Moreover, if the values reported in the second and in the third columns of Table VII are compared, it is easy to view that the utilization of BACS and TBM systems is able to reduce the consumption for heating and cooling allowing a considerable upgrade of the energy class of the building. TABLE XI CASH FLOWS FOR EVERY BUILDING ENERGY CLASS (VALUES IN ) Building year A B C D E F G VI. CONCLUSION In this paper a study has been presented correlating the costs for upgrading the BAC efficiency class of a test house from D to A with the economic return due to the reduction of the energy costs. The case study presented shows: - how the installation of a BAC or TBM system is as more convenient as higher is the electric and thermal consumption of the building and as lower is its energy class; - how the installation of a BAC or TBM system can improve the energy performance of a building and upgrade is energy class. A future work will be focused on this last very important point. REFERENCES [1] Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2019 on the energy performance of buildings. [2] Directive 2009/72/EC of the European Parliament and of the Council of 13 July 2009 concerning common rules for the internal market in electricity. [3] Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings. [4] European Technical Standard EN 15232, Performance of Buildings Impact of Building Automation, Control, and Building Management. [5] European Technical Standard EN 15217, performance of Buildings Methods for expressing energy performance and for the energy certification of buildings. [6] Italian Decree D.Lgs. 192/2005 of 19 August 2005 Attuazione della direttiva 2002/91/CE relativa al rendimento energetico nell'edilizia". [7] Italian Decree D.Lgs. 311/06 of 29 December 2006 Disposizioni correttive ed integrative al decreto legislativo 19 agosto 2005, n. 192, recante attuazione della direttiva 2002/91/CE, relativa al rendimento energetico nell'edilizia [8] Italian Ministerial Decree of the 26 giugno 2009 Linee guida nazionali per la certificazione energetica degli edifici. [9] European Technical Standard EN ISO 13790, performance of Buildings Calculaion of energy use for space heating and cooling. [10] A. Campoccia, E. Riva Sanseverino, G. Zizzo, A Montecarlo Approach for a Study on the Impact of the domestic installation of Small PV and Solar Systems on the grid., In Int. Conf. 7th World System Conference WESC 2008, Romania, pp [11] A. Capasso, W. Grattieri, R. Lamedica, A. Prudenzi, A Bottom-Up approach to residential load modelling, IEEE Transactions on Power Systems, vol. 9, no. 2, pp , May [12] A. Capasso, A. Invernizzi, R. Lamedica, A. Prudenzi, Probabilistic processing of survey collected data in residential load data for hourly demand profile extimation, In Int. Conf. IEEE/TUA Athens Power Conference: Planning, operation and control of Today s Electric Power Systems 1993, Greece, pp [13] A. Capasso, W. Grattieri, F. Insinga, A. Invernizzi, R. Lamedica, A. Prudenzi, Validation tests and applications of a model for demand-side management studies in residential load areas, In Int. Conference CIRED 2003, UK, pp. 5.25/1-5.25/5. [14] G. Ala, V. Cosentino, A. Di Stefano, G. Fiscelli, F. Genduso, G.C. Giaconia, M.G. Ippolito, D. La Cascia, F. Massaro, R. Miceli, P. Romano, C. Spataro, F. Viola, G. Zizzo, Management via Connected Household Appliances, McGraw-Hill, Italy, [15] EUROSTAT, Electricity and natural gas price statistics on May 2012,